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NCT07449182
An AI Educational Agent for Medical Machine Learning Courses
NA trial testing KGRAG-based AI Educational Agent System in Medical Education in 56 participants. Currently enrolling.
31 March 2026
Quick facts
| Lead sponsor | Sun Yat-sen University |
|---|---|
| Phase | NA |
| Status | Recruiting now |
| Study type | INTERVENTIONAL |
| Allocation | na |
| Design | single group |
| Masking | none |
| Primary purpose | other |
| Enrollment | 56 |
| Start date | 1 May 2025 |
| Primary completion | 31 March 2026 |
| Estimated completion | 31 March 2026 |
| Sites | 1 location across China |
Drugs / interventions tested
- KGRAG-based AI Educational Agent System
Conditions studied
- Medical Education — all drugs for Medical Education →
- Artificial Intelligence in Medicine — all drugs for Artificial Intelligence in Medicine →
Sponsor
Sun Yat-sen University
Who can join
Eligibility, any sex, with Medical Education or Artificial Intelligence in Medicine. Patients with the condition only — healthy volunteers not accepted.
Sponsor's own description
The goal of this interventional study is to evaluate the effectiveness of a Large Language Model (LLM)-based educational AI Agent in graduate students (Masters and PhD) specializing in medicine or nursing who are enrolled in the "Machine Learning and Data Mining" course. The main questions it aims to answer are: Does the use of an educational AI Agent improve students' academic performance and practical skills in machine learning compared to traditional methods? Does the AI intervention enhance students' learning confidence, satisfaction, and cognitive engagement? Researchers will compare students currently using the AI Agent (experimental group) to a historical control group (students from the previous cohort who did not use the AI tool) to see if the AI-assisted learning model leads to significantly higher learning achievements and better educational experiences. Participants will: Utilize the Teaching Agent for real-time answers to theoretical questions, personalized study planning, and knowledge reinforcement. Engage with the Research Agent to assist with literature reviews, research design optimization, and academic writing structure. Use the Practice Innovation Agent for guidance on coding, algorithm debugging, and applying machine learning models to medical data analysis projects.
Publications & conference data
No peer-reviewed publications indexed yet for this trial.
Verify or expand the search:
- PubMed search for NCT07449182
- Europe PMC full search
- ASCO Meeting Library
- ESMO Meeting Library
- bioRxiv preprints
- medRxiv preprints
- Google Scholar
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Verify against primary sources
- ClinicalTrials.gov — authoritative US registry record
- WHO ICTRP — international registry index
- EU Clinical Trials Register
- Sponsor press releases (Google)
- Trial protocol + status: ClinicalTrials.gov NCT07449182 (US National Library of Medicine, public domain)
- Drug + disease cross-links: matched in real time against Drug Landscape's normalised drug + company + condition tables
- Sponsor: as reported to ClinicalTrials.gov by Sun Yat-sen University
- Last refreshed: 5 March 2026
Drug Landscape aggregates and links these public records for informational use only. Always verify against the primary source before clinical or regulatory decisions. Canonical URL: https://druglandscape.com/trial/NCT07449182.
Primary sources · FDA · ClinicalTrials.gov · EMA · SEC EDGAR · ChEMBL · Wikidata · full sourcing